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All Journal Hanif Journal of Information Systems Al'Adzkiya International of Computer Science and Information Technology Journal
Akhmad Jayadi
Politeknik Negeri Lampung
Computer Science & IT Library & Information Science

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Improving the Accuracy of Lettuce and Weed Classification Based on MobileNetV2 Features Through Segmentation Akhmad Jayadi; Kurniawan Saputra; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

Automating the separation of commodity crops and weeds is a major challenge in the implementation of precision agriculture . The presence of complex backgrounds such as soil, rocks, and shadows often degrades the performance of feature extraction in computer vision classification models. This study proposes an image preprocessing approach using the GrabCut segmentation method to extract key crop objects cleanly before performing Deep Learning- based feature extraction . Representative features from the image are extracted using the lightweight and efficient MobileNetV2 architecture. Next, classification is performed by comparing three Machine Learning algorithms , namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Testing is carried out on two data scenarios, namely the original dataset ( Original ) and the segmented dataset ( GrabCut ). The experimental results show that the use of original images produces an accuracy of 98.89% for all three classification models. However, after being integrated with GrabCut segmentation, the accuracy of all three models increases significantly to 100.00%. These results prove that GrabCut-based segmentation effectively eliminates background noise information , thereby improving the generalization capabilities of classification models perfectly on edge computing devices .
Comparative Analysis of Radix Sort, Quick Sort, and Bubble Sort Algorithms in Data Sorting Based on Array Size and Time Kurnia Wati Fadhillah; Nandy Thaher Ulga; Raffi Ramadhan Oktaviansyah; Farah Sulistia; Jeni Amanda; Akhmad Jayadi
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v6i2.354

Abstract

An algorithm is a series of logical actions used to solve important problems in contemporary programming and data processing. The purpose of this study is to compare the time efficiency of three sorting algorithms: Bubble Sort, Radix Sort, and Quick Sort. All algorithms are used on small (10-100 elements), medium (1,000-10,000 elements), and large (more than 100,000 elements) arrays, with execution time using Java. The results show that Radix Sort and Quick Sort are generally more efficient and scalable than Bubble Sort, especially for large arrays and random or semi-sorted data. Radix Sort excels on small and medium arrays under various conditions, while Quick Sort excels on large arrays in the average and nearly sorted cases. Although Bubble Sort can be the fastest in the best case for large arrays, its performance drops drastically in the average and nearly sorted cases. In conclusion, the selection of the best sorting algorithm depends heavily on the type of input data, such as its size and the degree of initial sorting.
Co-Authors Ahmad Rofi'i Farah Sulistia Jeni Amanda Kurnia Wati Fadhillah Kurniawan Saputra Nandy Thaher Ulga Raffi Ramadhan Oktaviansyah